spark PythonSQLUtils 源码

spark PythonSQLUtils 代码

文件路径：/sql/core/src/main/scala/org/apache/spark/sql/api/python/PythonSQLUtils.scala

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.spark.sql.api.python

import java.io.InputStream
import java.net.Socket
import java.nio.channels.Channels
import java.util.Locale

import net.razorvine.pickle.{Pickler, Unpickler}

import org.apache.spark.api.python.DechunkedInputStream
import org.apache.spark.internal.Logging
import org.apache.spark.security.SocketAuthServer
import org.apache.spark.sql.{Column, DataFrame, Row, SparkSession}
import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.aggregate._
import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
import org.apache.spark.sql.execution.{ExplainMode, QueryExecution}
import org.apache.spark.sql.execution.arrow.ArrowConverters
import org.apache.spark.sql.execution.python.EvaluatePython
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.{DataType, StructType}

private[sql] object PythonSQLUtils extends Logging {
  private def withInternalRowPickler(f: Pickler => Array[Byte]): Array[Byte] = {
    EvaluatePython.registerPicklers()
    val pickler = new Pickler(true, false)
    val ret = try {
        f(pickler)
      } finally {
        pickler.close()
      }
    ret
  }

  private def withInternalRowUnpickler(f: Unpickler => Any): Any = {
    EvaluatePython.registerPicklers()
    val unpickler = new Unpickler
    val ret = try {
        f(unpickler)
      } finally {
        unpickler.close()
      }
    ret
  }

  def parseDataType(typeText: String): DataType = CatalystSqlParser.parseDataType(typeText)

  // This is needed when generating SQL documentation for built-in functions.
  def listBuiltinFunctionInfos(): Array[ExpressionInfo] = {
    FunctionRegistry.functionSet.flatMap(f => FunctionRegistry.builtin.lookupFunction(f)).toArray
  }

  private def listAllSQLConfigs(): Seq[(String, String, String, String)] = {
    val conf = new SQLConf()
    conf.getAllDefinedConfs
  }

  def listRuntimeSQLConfigs(): Array[(String, String, String, String)] = {
    // Py4J doesn't seem to translate Seq well, so we convert to an Array.
    listAllSQLConfigs().filterNot(p => SQLConf.isStaticConfigKey(p._1)).toArray
  }

  def listStaticSQLConfigs(): Array[(String, String, String, String)] = {
    listAllSQLConfigs().filter(p => SQLConf.isStaticConfigKey(p._1)).toArray
  }

  def isTimestampNTZPreferred: Boolean =
    SQLConf.get.timestampType == org.apache.spark.sql.types.TimestampNTZType

  /**
   * Python callable function to read a file in Arrow stream format and create an iterator
   * of serialized ArrowRecordBatches.
   */
  def readArrowStreamFromFile(filename: String): Iterator[Array[Byte]] = {
    ArrowConverters.readArrowStreamFromFile(filename).iterator
  }

  /**
   * Python callable function to read a file in Arrow stream format and create a [[DataFrame]]
   * from the Arrow batch iterator.
   */
  def toDataFrame(
      arrowBatches: Iterator[Array[Byte]],
      schemaString: String,
      session: SparkSession): DataFrame = {
    ArrowConverters.toDataFrame(arrowBatches, schemaString, session)
  }

  def explainString(queryExecution: QueryExecution, mode: String): String = {
    queryExecution.explainString(ExplainMode.fromString(mode))
  }

  def toPyRow(row: Row): Array[Byte] = {
    assert(row.isInstanceOf[GenericRowWithSchema])
    withInternalRowPickler(_.dumps(EvaluatePython.toJava(
      CatalystTypeConverters.convertToCatalyst(row), row.schema)))
  }

  def toJVMRow(
      arr: Array[Byte],
      returnType: StructType,
      deserializer: ExpressionEncoder.Deserializer[Row]): Row = {
    val fromJava = EvaluatePython.makeFromJava(returnType)
    val internalRow =
        fromJava(withInternalRowUnpickler(_.loads(arr))).asInstanceOf[InternalRow]
    deserializer(internalRow)
  }

  def castTimestampNTZToLong(c: Column): Column = Column(CastTimestampNTZToLong(c.expr))

  def ewm(e: Column, alpha: Double, ignoreNA: Boolean): Column =
    Column(EWM(e.expr, alpha, ignoreNA))

  def lastNonNull(e: Column): Column = Column(LastNonNull(e.expr))

  def nullIndex(e: Column): Column = Column(NullIndex(e.expr))

  def makeInterval(unit: String, e: Column): Column = {
    val zero = MakeInterval(years = Literal(0), months = Literal(0), weeks = Literal(0),
      days = Literal(0), hours = Literal(0), mins = Literal(0), secs = Literal(0))

    unit.toUpperCase(Locale.ROOT) match {
      case "YEAR" => Column(zero.copy(years = e.expr))
      case "MONTH" => Column(zero.copy(months = e.expr))
      case "WEEK" => Column(zero.copy(weeks = e.expr))
      case "DAY" => Column(zero.copy(days = e.expr))
      case "HOUR" => Column(zero.copy(hours = e.expr))
      case "MINUTE" => Column(zero.copy(mins = e.expr))
      case "SECOND" => Column(zero.copy(secs = e.expr))
      case _ => throw new IllegalStateException(s"Got the unexpected unit '$unit'.")
    }
  }

  def timestampDiff(unit: String, start: Column, end: Column): Column = {
    Column(TimestampDiff(unit, start.expr, end.expr))
  }

  def pandasProduct(e: Column, ignoreNA: Boolean): Column = {
    Column(PandasProduct(e.expr, ignoreNA).toAggregateExpression(false))
  }

  def pandasStddev(e: Column, ddof: Int): Column = {
    Column(PandasStddev(e.expr, ddof).toAggregateExpression(false))
  }

  def pandasVariance(e: Column, ddof: Int): Column = {
    Column(PandasVariance(e.expr, ddof).toAggregateExpression(false))
  }

  def pandasSkewness(e: Column): Column = {
    Column(PandasSkewness(e.expr).toAggregateExpression(false))
  }

  def pandasKurtosis(e: Column): Column = {
    Column(PandasKurtosis(e.expr).toAggregateExpression(false))
  }

  def pandasMode(e: Column, ignoreNA: Boolean): Column = {
    Column(PandasMode(e.expr, ignoreNA).toAggregateExpression(false))
  }

  def pandasCovar(col1: Column, col2: Column, ddof: Int): Column = {
    Column(PandasCovar(col1.expr, col2.expr, ddof).toAggregateExpression(false))
  }
}

/**
 * Helper for making a dataframe from Arrow data from data sent from python over a socket. This is
 * used when encryption is enabled, and we don't want to write data to a file.
 */
private[spark] class ArrowIteratorServer
  extends SocketAuthServer[Iterator[Array[Byte]]]("pyspark-arrow-batches-server") {

  def handleConnection(sock: Socket): Iterator[Array[Byte]] = {
    val in = sock.getInputStream()
    val dechunkedInput: InputStream = new DechunkedInputStream(in)
    // Create array to consume iterator so that we can safely close the file
    ArrowConverters.getBatchesFromStream(Channels.newChannel(dechunkedInput)).toArray.iterator
  }
}

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